Satellite Passive Microwave Sea-Ice Concentration Data Set Intercomparison: Closed Ice and Ship-Based Observations

Stefan Kern*, Thomas Lavergne, Dirk Notz, Leif Toudal Pedersen, Rasmus Tage Tonboe, Roberto Saldo, Atle MacDonald Soerensen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

We report on results of a systematic inter-comparison of 10 global sea-ice concentration (SIC) data products at 12.5 to 50.0 km grid resolution for both the Arctic and the Antarctic. The products are compared with each other with respect to differences in SIC, sea-ice area (SIA), and sea-ice extent (SIE), and they are compared against a global wintertime near-100 % reference SIC data set for closed pack ice conditions and against global year-round ship-based visual observations of the sea-ice cover. We can group the products based on the concept of their SIC retrieval algorithms. Group I consists of data sets using the self-optimizing EUMETSAT OSI SAF and ESA CCI algorithms. Group II includes data using the Comiso bootstrap algorithm and the NOAA NSIDC sea-ice concentration climate data record (CDR). The standard NASA Team and the ARTIST Sea Ice (ASI) algorithms are put into group III, and NASA Team 2 is the only element of group IV. The three CDRs of group I (SICCI-25km, SICCI-50km, and OSI-450) are biased low compared to a 100 % reference SIC data set with biases of −0.4 % to −1.0 % (Arctic) and −0.3 % to −1.1 % (Antarctic). Products of group II appear to be mostly biased high in the Arctic by between +1.0 % and +3.5 %, while their biases in the Antarctic range from −0.2 % to +0.9 %. Group III product biases are different for the Arctic, +0.9 % (NASA Team) and −3.7 % (ASI), but similar for the Antarctic, −5.4 % and −5.6 %, respectively. The standard deviation is smaller in the Arctic for the quoted group I products (1.9 % to 2.9 %) and Antarctic (2.5 % to 3.1 %) than for group II and III products: 3.6 % to 5.0 % for the Arctic and 4.0 % to 6.5 % for the Antarctic. We refer to the paper to understand why we could not give values for group IV here. We discuss the impact of truncating the SIC distribution, as naturally retrieved by the algorithms around the 100 % sea-ice concentration end. We show that evaluation studies of such truncated SIC products can result in misleading statistics and favour data sets that systematically overestimate SIC. We describe a method to reconstruct the non-truncated distribution of SIC before the evaluation is performed. On the basis of this evaluation, we open a discussion about the overestimation of SIC in data products, with far-reaching consequences for surface heat flux estimations in winter. We also document inconsistencies in the behaviour of the weather filters used in products of group II, and we suggest advancing studies about the influence of these weather filters on SIA and SIE time series and their trends.
Original languageEnglish
JournalCryosphere
Volume13
Pages (from-to)3261-3307
Number of pages47
ISSN1994-0416
DOIs
Publication statusPublished - 2019

Cite this

Kern, Stefan ; Lavergne, Thomas ; Notz, Dirk ; Pedersen, Leif Toudal ; Tonboe, Rasmus Tage ; Saldo, Roberto ; Soerensen, Atle MacDonald. / Satellite Passive Microwave Sea-Ice Concentration Data Set Intercomparison: Closed Ice and Ship-Based Observations. In: Cryosphere. 2019 ; Vol. 13. pp. 3261-3307.
@article{481f159bf7da48bdb64ec99691f67c6c,
title = "Satellite Passive Microwave Sea-Ice Concentration Data Set Intercomparison: Closed Ice and Ship-Based Observations",
abstract = "We report on results of a systematic inter-comparison of 10 global sea-ice concentration (SIC) data products at 12.5 to 50.0 km grid resolution for both the Arctic and the Antarctic. The products are compared with each other with respect to differences in SIC, sea-ice area (SIA), and sea-ice extent (SIE), and they are compared against a global wintertime near-100 {\%} reference SIC data set for closed pack ice conditions and against global year-round ship-based visual observations of the sea-ice cover. We can group the products based on the concept of their SIC retrieval algorithms. Group I consists of data sets using the self-optimizing EUMETSAT OSI SAF and ESA CCI algorithms. Group II includes data using the Comiso bootstrap algorithm and the NOAA NSIDC sea-ice concentration climate data record (CDR). The standard NASA Team and the ARTIST Sea Ice (ASI) algorithms are put into group III, and NASA Team 2 is the only element of group IV. The three CDRs of group I (SICCI-25km, SICCI-50km, and OSI-450) are biased low compared to a 100 {\%} reference SIC data set with biases of −0.4 {\%} to −1.0 {\%} (Arctic) and −0.3 {\%} to −1.1 {\%} (Antarctic). Products of group II appear to be mostly biased high in the Arctic by between +1.0 {\%} and +3.5 {\%}, while their biases in the Antarctic range from −0.2 {\%} to +0.9 {\%}. Group III product biases are different for the Arctic, +0.9 {\%} (NASA Team) and −3.7 {\%} (ASI), but similar for the Antarctic, −5.4 {\%} and −5.6 {\%}, respectively. The standard deviation is smaller in the Arctic for the quoted group I products (1.9 {\%} to 2.9 {\%}) and Antarctic (2.5 {\%} to 3.1 {\%}) than for group II and III products: 3.6 {\%} to 5.0 {\%} for the Arctic and 4.0 {\%} to 6.5 {\%} for the Antarctic. We refer to the paper to understand why we could not give values for group IV here. We discuss the impact of truncating the SIC distribution, as naturally retrieved by the algorithms around the 100 {\%} sea-ice concentration end. We show that evaluation studies of such truncated SIC products can result in misleading statistics and favour data sets that systematically overestimate SIC. We describe a method to reconstruct the non-truncated distribution of SIC before the evaluation is performed. On the basis of this evaluation, we open a discussion about the overestimation of SIC in data products, with far-reaching consequences for surface heat flux estimations in winter. We also document inconsistencies in the behaviour of the weather filters used in products of group II, and we suggest advancing studies about the influence of these weather filters on SIA and SIE time series and their trends.",
author = "Stefan Kern and Thomas Lavergne and Dirk Notz and Pedersen, {Leif Toudal} and Tonboe, {Rasmus Tage} and Roberto Saldo and Soerensen, {Atle MacDonald}",
year = "2019",
doi = "10.5194/tc-13-3261-2019",
language = "English",
volume = "13",
pages = "3261--3307",
journal = "Cryosphere",
issn = "1994-0416",
publisher = "Copernicus GmbH",

}

Satellite Passive Microwave Sea-Ice Concentration Data Set Intercomparison: Closed Ice and Ship-Based Observations. / Kern, Stefan; Lavergne, Thomas; Notz, Dirk; Pedersen, Leif Toudal; Tonboe, Rasmus Tage; Saldo, Roberto; Soerensen, Atle MacDonald.

In: Cryosphere, Vol. 13, 2019, p. 3261-3307.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Satellite Passive Microwave Sea-Ice Concentration Data Set Intercomparison: Closed Ice and Ship-Based Observations

AU - Kern, Stefan

AU - Lavergne, Thomas

AU - Notz, Dirk

AU - Pedersen, Leif Toudal

AU - Tonboe, Rasmus Tage

AU - Saldo, Roberto

AU - Soerensen, Atle MacDonald

PY - 2019

Y1 - 2019

N2 - We report on results of a systematic inter-comparison of 10 global sea-ice concentration (SIC) data products at 12.5 to 50.0 km grid resolution for both the Arctic and the Antarctic. The products are compared with each other with respect to differences in SIC, sea-ice area (SIA), and sea-ice extent (SIE), and they are compared against a global wintertime near-100 % reference SIC data set for closed pack ice conditions and against global year-round ship-based visual observations of the sea-ice cover. We can group the products based on the concept of their SIC retrieval algorithms. Group I consists of data sets using the self-optimizing EUMETSAT OSI SAF and ESA CCI algorithms. Group II includes data using the Comiso bootstrap algorithm and the NOAA NSIDC sea-ice concentration climate data record (CDR). The standard NASA Team and the ARTIST Sea Ice (ASI) algorithms are put into group III, and NASA Team 2 is the only element of group IV. The three CDRs of group I (SICCI-25km, SICCI-50km, and OSI-450) are biased low compared to a 100 % reference SIC data set with biases of −0.4 % to −1.0 % (Arctic) and −0.3 % to −1.1 % (Antarctic). Products of group II appear to be mostly biased high in the Arctic by between +1.0 % and +3.5 %, while their biases in the Antarctic range from −0.2 % to +0.9 %. Group III product biases are different for the Arctic, +0.9 % (NASA Team) and −3.7 % (ASI), but similar for the Antarctic, −5.4 % and −5.6 %, respectively. The standard deviation is smaller in the Arctic for the quoted group I products (1.9 % to 2.9 %) and Antarctic (2.5 % to 3.1 %) than for group II and III products: 3.6 % to 5.0 % for the Arctic and 4.0 % to 6.5 % for the Antarctic. We refer to the paper to understand why we could not give values for group IV here. We discuss the impact of truncating the SIC distribution, as naturally retrieved by the algorithms around the 100 % sea-ice concentration end. We show that evaluation studies of such truncated SIC products can result in misleading statistics and favour data sets that systematically overestimate SIC. We describe a method to reconstruct the non-truncated distribution of SIC before the evaluation is performed. On the basis of this evaluation, we open a discussion about the overestimation of SIC in data products, with far-reaching consequences for surface heat flux estimations in winter. We also document inconsistencies in the behaviour of the weather filters used in products of group II, and we suggest advancing studies about the influence of these weather filters on SIA and SIE time series and their trends.

AB - We report on results of a systematic inter-comparison of 10 global sea-ice concentration (SIC) data products at 12.5 to 50.0 km grid resolution for both the Arctic and the Antarctic. The products are compared with each other with respect to differences in SIC, sea-ice area (SIA), and sea-ice extent (SIE), and they are compared against a global wintertime near-100 % reference SIC data set for closed pack ice conditions and against global year-round ship-based visual observations of the sea-ice cover. We can group the products based on the concept of their SIC retrieval algorithms. Group I consists of data sets using the self-optimizing EUMETSAT OSI SAF and ESA CCI algorithms. Group II includes data using the Comiso bootstrap algorithm and the NOAA NSIDC sea-ice concentration climate data record (CDR). The standard NASA Team and the ARTIST Sea Ice (ASI) algorithms are put into group III, and NASA Team 2 is the only element of group IV. The three CDRs of group I (SICCI-25km, SICCI-50km, and OSI-450) are biased low compared to a 100 % reference SIC data set with biases of −0.4 % to −1.0 % (Arctic) and −0.3 % to −1.1 % (Antarctic). Products of group II appear to be mostly biased high in the Arctic by between +1.0 % and +3.5 %, while their biases in the Antarctic range from −0.2 % to +0.9 %. Group III product biases are different for the Arctic, +0.9 % (NASA Team) and −3.7 % (ASI), but similar for the Antarctic, −5.4 % and −5.6 %, respectively. The standard deviation is smaller in the Arctic for the quoted group I products (1.9 % to 2.9 %) and Antarctic (2.5 % to 3.1 %) than for group II and III products: 3.6 % to 5.0 % for the Arctic and 4.0 % to 6.5 % for the Antarctic. We refer to the paper to understand why we could not give values for group IV here. We discuss the impact of truncating the SIC distribution, as naturally retrieved by the algorithms around the 100 % sea-ice concentration end. We show that evaluation studies of such truncated SIC products can result in misleading statistics and favour data sets that systematically overestimate SIC. We describe a method to reconstruct the non-truncated distribution of SIC before the evaluation is performed. On the basis of this evaluation, we open a discussion about the overestimation of SIC in data products, with far-reaching consequences for surface heat flux estimations in winter. We also document inconsistencies in the behaviour of the weather filters used in products of group II, and we suggest advancing studies about the influence of these weather filters on SIA and SIE time series and their trends.

U2 - 10.5194/tc-13-3261-2019

DO - 10.5194/tc-13-3261-2019

M3 - Journal article

VL - 13

SP - 3261

EP - 3307

JO - Cryosphere

JF - Cryosphere

SN - 1994-0416

ER -